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New analysis framework incorporating mixed mutual information and scalable Bayesian networks for multimodal high dimensional genomic and epigenomic cancer data

Xichun Wang, Sergio Branciamore, Grigoriy Gogoshin, Shuyu Ding, Andrei S Rodin
doi: https://doi.org/10.1101/812446
Xichun Wang
Diabetes and Metabolism Research Institute and Beckman Research Institute of the City of Hope, Duarte, CA 91010
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Sergio Branciamore
Diabetes and Metabolism Research Institute and Beckman Research Institute of the City of Hope, Duarte, CA 91010
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Grigoriy Gogoshin
Diabetes and Metabolism Research Institute and Beckman Research Institute of the City of Hope, Duarte, CA 91010
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Shuyu Ding
Diabetes and Metabolism Research Institute and Beckman Research Institute of the City of Hope, Duarte, CA 91010
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Andrei S Rodin
Diabetes and Metabolism Research Institute and Beckman Research Institute of the City of Hope, Duarte, CA 91010
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  • For correspondence: arodin@coh.org
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Abstract

We propose a novel two-stage analysis strategy to discover candidate genes associated with the particular cancer outcomes in large multimodal genomic cancers databases, such as The Cancer Genome Atlas (TCGA). During the first stage, we use mixed mutual information to perform variable selection; during the second stage, we use scalable Bayesian network (BN) modeling to identify candidate genes and their interactions. Two crucial features of the proposed approach are (i) the ability to handle mixed data types (continuous and discrete, genomic, epigenomic, etc.), and (ii) a flexible boundary between the variable selection and network modeling stages --- the boundary that can be adjusted in accordance with the investigators’ BN software scalability and hardware implementation. These two aspects result in high generalizability of the proposed analytical framework. We apply the above strategy to three different TCGA datasets (LGG, Brain Lower Grade Glioma; HNSC, Head and Neck Squamous Cell Carcinoma; STES, Stomach and Esophageal Carcinoma), linking multimodal molecular information (SNPs, mRNA expression, DNA methylation) to two clinical outcome variables (tumor status and patient survival). We identify 11 candidate genes, of which 6 have already been directly implicated in the cancer literature. One novel LGG prognostic factor suggested by our analysis, methylation of TMPRSS11F type II transmembrane serine protease, presents intriguing direction for the follow-up studies.

Footnotes

  • xicwang{at}coh.org, sbranciamore{at}coh.org, ggogoshin{at}coh.org, shuyudings{at}gmail.com, arodin{at}coh.org

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Posted October 21, 2019.
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New analysis framework incorporating mixed mutual information and scalable Bayesian networks for multimodal high dimensional genomic and epigenomic cancer data
Xichun Wang, Sergio Branciamore, Grigoriy Gogoshin, Shuyu Ding, Andrei S Rodin
bioRxiv 812446; doi: https://doi.org/10.1101/812446
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New analysis framework incorporating mixed mutual information and scalable Bayesian networks for multimodal high dimensional genomic and epigenomic cancer data
Xichun Wang, Sergio Branciamore, Grigoriy Gogoshin, Shuyu Ding, Andrei S Rodin
bioRxiv 812446; doi: https://doi.org/10.1101/812446

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